Machine Learning Framework for Multi-Endpoint Quantum Dot Toxicity Prediction with Organoid Validation and Drug...

Area of Science:

• Nanotechnology
• Toxicology
• Biomedical Engineering

Background:

• Quantum dots (QDs) offer unique properties for biomedicine and optoelectronics.
• Their nanoscale nature and surface chemistry raise concerns about potential toxicity.
• A systematic approach is needed for comprehensive QD safety assessment.

Purpose of the Study:

• To establish a multi-endpoint framework for evaluating quantum dot toxicity.
• To build and validate machine learning models for predicting QD-induced toxic effects.
• To identify potential intervention strategies for mitigating QD toxicity.

Main Methods:

• Collected physicochemical properties and toxicity endpoints (cell death, inflammation, oxidative stress) of various QDs.
• Developed machine learning models (Random Forest, XGBoost, KNN, SVM) for toxicity prediction.
• Validated predictions using brain organoids and employed SHAP analysis for feature importance.

Main Results:

• Machine learning models successfully predicted QD toxicity.
• Exposure dose and particle size were identified as key drivers of toxicity across models.
• Zeta potential and optical properties influenced specific toxicity endpoints.
• Carfilzomib showed strong binding to core targets, indicating potential as an intervention drug.

Conclusions:

• The developed framework provides a systematic approach for evaluating multi-endpoint QD toxicity.
• This study offers a robust method for nanomaterial safety assessment and intervention strategy development.
• Findings contribute to the safe application of quantum dots in various fields.